Overview

Dataset statistics

Number of variables21
Number of observations21436
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 MiB
Average record size in memory200.7 B

Variable types

Numeric17
DateTime1
Categorical3

Alerts

bathrooms is highly overall correlated with price and 7 other fieldsHigh correlation
bedrooms is highly overall correlated with bathrooms and 2 other fieldsHigh correlation
floors is highly overall correlated with yr_builtHigh correlation
grade is highly overall correlated with price and 4 other fieldsHigh correlation
long is highly overall correlated with yr_built and 1 other fieldsHigh correlation
price is highly overall correlated with bathrooms and 5 other fieldsHigh correlation
sqft_above is highly overall correlated with price and 6 other fieldsHigh correlation
sqft_living is highly overall correlated with price and 6 other fieldsHigh correlation
sqft_living15 is highly overall correlated with price and 4 other fieldsHigh correlation
sqft_lot is highly overall correlated with sqft_lot15High correlation
sqft_lot15 is highly overall correlated with sqft_lotHigh correlation
view is highly overall correlated with waterfrontHigh correlation
waterfront is highly overall correlated with viewHigh correlation
yr_built is highly overall correlated with bathrooms and 4 other fieldsHigh correlation
zipcode is highly overall correlated with yr_built and 2 other fieldsHigh correlation
condition is highly overall correlated with yr_builtHigh correlation
sqft_basement is highly overall correlated with price and 3 other fieldsHigh correlation
lat is highly overall correlated with zipcodeHigh correlation
waterfront is highly imbalanced (93.6%)Imbalance
view is highly imbalanced (72.2%)Imbalance
id has unique valuesUnique
sqft_basement has 13015 (60.7%) zerosZeros
yr_renovated has 20526 (95.8%) zerosZeros

Reproduction

Analysis started2024-11-26 14:08:51.757593
Analysis finished2024-11-26 14:09:26.802360
Duration35.04 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct21436
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5807653 × 109
Minimum1000102
Maximum9.9000002 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:26.891562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1000102
5-th percentile5.1000319 × 108
Q12.1237001 × 109
median3.9049212 × 109
Q37.3086751 × 109
95-th percentile9.2973006 × 109
Maximum9.9000002 × 109
Range9.8990001 × 109
Interquartile range (IQR)5.184975 × 109

Descriptive statistics

Standard deviation2.8765896 × 109
Coefficient of variation (CV)0.6279714
Kurtosis-1.2605654
Mean4.5807653 × 109
Median Absolute Deviation (MAD)2.4027999 × 109
Skewness0.24323134
Sum9.8193286 × 1013
Variance8.2747679 × 1018
MonotonicityNot monotonic
2024-11-26T22:09:27.476171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7129300520 1
 
< 0.1%
8929000230 1
 
< 0.1%
9543000205 1
 
< 0.1%
8137500730 1
 
< 0.1%
104500730 1
 
< 0.1%
7575610760 1
 
< 0.1%
629800540 1
 
< 0.1%
7215730120 1
 
< 0.1%
2064800610 1
 
< 0.1%
3577300040 1
 
< 0.1%
Other values (21426) 21426
> 99.9%
ValueCountFrequency (%)
1000102 1
< 0.1%
1200019 1
< 0.1%
1200021 1
< 0.1%
2800031 1
< 0.1%
3600057 1
< 0.1%
3600072 1
< 0.1%
3800008 1
< 0.1%
5200087 1
< 0.1%
6200017 1
< 0.1%
7200080 1
< 0.1%
ValueCountFrequency (%)
9900000190 1
< 0.1%
9895000040 1
< 0.1%
9842300540 1
< 0.1%
9842300485 1
< 0.1%
9842300095 1
< 0.1%
9842300036 1
< 0.1%
9839301165 1
< 0.1%
9839301060 1
< 0.1%
9839301055 1
< 0.1%
9839300875 1
< 0.1%

date
Date

Distinct372
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size851.0 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-27 00:00:00
2024-11-26T22:09:27.604111image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:27.742807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

price
Real number (ℝ)

HIGH CORRELATION 

Distinct3997
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean541649.96
Minimum75000
Maximum7700000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:27.899362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum75000
5-th percentile213500
Q1324866
median450000
Q3645000
95-th percentile1160000
Maximum7700000
Range7625000
Interquartile range (IQR)320134

Descriptive statistics

Standard deviation367314.93
Coefficient of variation (CV)0.67814078
Kurtosis34.725226
Mean541649.96
Median Absolute Deviation (MAD)150000
Skewness4.0361072
Sum1.1610809 × 1010
Variance1.3492026 × 1011
MonotonicityNot monotonic
2024-11-26T22:09:28.063816image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450000 172
 
0.8%
350000 167
 
0.8%
550000 157
 
0.7%
500000 151
 
0.7%
425000 149
 
0.7%
325000 146
 
0.7%
400000 144
 
0.7%
375000 137
 
0.6%
300000 131
 
0.6%
525000 128
 
0.6%
Other values (3987) 19954
93.1%
ValueCountFrequency (%)
75000 1
< 0.1%
78000 1
< 0.1%
80000 1
< 0.1%
81000 1
< 0.1%
82500 1
< 0.1%
83000 1
< 0.1%
84000 1
< 0.1%
85000 1
< 0.1%
89000 1
< 0.1%
89950 1
< 0.1%
ValueCountFrequency (%)
7700000 1
< 0.1%
7062500 1
< 0.1%
6885000 1
< 0.1%
5570000 1
< 0.1%
5350000 1
< 0.1%
5300000 1
< 0.1%
5110800 1
< 0.1%
4668000 1
< 0.1%
4500000 1
< 0.1%
4489000 1
< 0.1%

bedrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3715712
Minimum0
Maximum33
Zeros13
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:28.198950image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum33
Range33
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.92920466
Coefficient of variation (CV)0.27559989
Kurtosis49.638224
Mean3.3715712
Median Absolute Deviation (MAD)1
Skewness1.9898602
Sum72273
Variance0.8634213
MonotonicityNot monotonic
2024-11-26T22:09:28.305926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 9731
45.4%
4 6849
32.0%
2 2736
 
12.8%
5 1586
 
7.4%
6 265
 
1.2%
1 194
 
0.9%
7 38
 
0.2%
0 13
 
0.1%
8 13
 
0.1%
9 6
 
< 0.1%
Other values (3) 5
 
< 0.1%
ValueCountFrequency (%)
0 13
 
0.1%
1 194
 
0.9%
2 2736
 
12.8%
3 9731
45.4%
4 6849
32.0%
5 1586
 
7.4%
6 265
 
1.2%
7 38
 
0.2%
8 13
 
0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
33 1
 
< 0.1%
11 1
 
< 0.1%
10 3
 
< 0.1%
9 6
 
< 0.1%
8 13
 
0.1%
7 38
 
0.2%
6 265
 
1.2%
5 1586
 
7.4%
4 6849
32.0%
3 9731
45.4%

bathrooms
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1173493
Minimum0
Maximum8
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:28.424278image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range8
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.76991279
Coefficient of variation (CV)0.36362105
Kurtosis1.2914875
Mean2.1173493
Median Absolute Deviation (MAD)0.5
Skewness0.51018057
Sum45387.5
Variance0.5927657
MonotonicityNot monotonic
2024-11-26T22:09:28.542984image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
2.5 5355
25.0%
1 3795
17.7%
1.75 3020
14.1%
2.25 2031
 
9.5%
2 1913
 
8.9%
1.5 1430
 
6.7%
2.75 1182
 
5.5%
3 747
 
3.5%
3.5 729
 
3.4%
3.25 586
 
2.7%
Other values (20) 648
 
3.0%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.5 4
 
< 0.1%
0.75 71
 
0.3%
1 3795
17.7%
1.25 9
 
< 0.1%
1.5 1430
 
6.7%
1.75 3020
14.1%
2 1913
 
8.9%
2.25 2031
 
9.5%
2.5 5355
25.0%
ValueCountFrequency (%)
8 2
 
< 0.1%
7.75 1
 
< 0.1%
7.5 1
 
< 0.1%
6.75 2
 
< 0.1%
6.5 2
 
< 0.1%
6.25 2
 
< 0.1%
6 6
< 0.1%
5.75 4
 
< 0.1%
5.5 10
< 0.1%
5.25 13
0.1%

sqft_living
Real number (ℝ)

HIGH CORRELATION 

Distinct1038
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2082.7049
Minimum290
Maximum13540
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:28.678271image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile940
Q11430
median1920
Q32550
95-th percentile3770
Maximum13540
Range13250
Interquartile range (IQR)1120

Descriptive statistics

Standard deviation919.14647
Coefficient of variation (CV)0.44132342
Kurtosis5.2490757
Mean2082.7049
Median Absolute Deviation (MAD)550
Skewness1.471021
Sum44644863
Variance844830.23
MonotonicityNot monotonic
2024-11-26T22:09:28.811562image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 136
 
0.6%
1440 133
 
0.6%
1400 132
 
0.6%
1660 128
 
0.6%
1800 128
 
0.6%
1820 127
 
0.6%
1560 124
 
0.6%
1010 124
 
0.6%
1480 122
 
0.6%
1540 122
 
0.6%
Other values (1028) 20160
94.0%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
13540 1
< 0.1%
12050 1
< 0.1%
10040 1
< 0.1%
9890 1
< 0.1%
9640 1
< 0.1%
9200 1
< 0.1%
8670 1
< 0.1%
8020 1
< 0.1%
8010 1
< 0.1%
8000 1
< 0.1%

sqft_lot
Real number (ℝ)

HIGH CORRELATION 

Distinct9782
Distinct (%)45.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15135.638
Minimum520
Maximum1651359
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:28.944020image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum520
5-th percentile1800
Q15040
median7614
Q310696.25
95-th percentile43560
Maximum1651359
Range1650839
Interquartile range (IQR)5656.25

Descriptive statistics

Standard deviation41538.621
Coefficient of variation (CV)2.7444248
Kurtosis284.08354
Mean15135.638
Median Absolute Deviation (MAD)2615.5
Skewness13.043673
Sum3.2444753 × 108
Variance1.725457 × 109
MonotonicityNot monotonic
2024-11-26T22:09:29.084703image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 355
 
1.7%
6000 286
 
1.3%
4000 249
 
1.2%
7200 218
 
1.0%
4800 118
 
0.6%
7500 118
 
0.6%
4500 112
 
0.5%
8400 109
 
0.5%
9600 108
 
0.5%
3600 102
 
0.5%
Other values (9772) 19661
91.7%
ValueCountFrequency (%)
520 1
< 0.1%
572 1
< 0.1%
600 1
< 0.1%
609 1
< 0.1%
635 1
< 0.1%
638 1
< 0.1%
649 2
< 0.1%
651 1
< 0.1%
675 1
< 0.1%
676 1
< 0.1%
ValueCountFrequency (%)
1651359 1
< 0.1%
1164794 1
< 0.1%
1074218 1
< 0.1%
1024068 1
< 0.1%
982998 1
< 0.1%
982278 1
< 0.1%
920423 1
< 0.1%
881654 1
< 0.1%
871200 2
< 0.1%
843309 1
< 0.1%

floors
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.496198
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:29.202727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1.5
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.54038838
Coefficient of variation (CV)0.36117438
Kurtosis-0.49077396
Mean1.496198
Median Absolute Deviation (MAD)0.5
Skewness0.61047946
Sum32072.5
Variance0.2920196
MonotonicityNot monotonic
2024-11-26T22:09:29.323251image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 10559
49.3%
2 8209
38.3%
1.5 1888
 
8.8%
3 611
 
2.9%
2.5 161
 
0.8%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
1 10559
49.3%
1.5 1888
 
8.8%
2 8209
38.3%
2.5 161
 
0.8%
3 611
 
2.9%
3.5 8
 
< 0.1%
ValueCountFrequency (%)
3.5 8
 
< 0.1%
3 611
 
2.9%
2.5 161
 
0.8%
2 8209
38.3%
1.5 1888
 
8.8%
1 10559
49.3%

waterfront
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size851.0 KiB
0
21273 
1
 
163

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Length

2024-11-26T22:09:29.441821image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T22:09:29.538238image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring characters

ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 21273
99.2%
1 163
 
0.8%

view
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size851.0 KiB
0
19320 
2
 
962
3
 
507
1
 
331
4
 
316

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Length

2024-11-26T22:09:29.632420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T22:09:29.729434image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 19320
90.1%
2 962
 
4.5%
3 507
 
2.4%
1 331
 
1.5%
4 316
 
1.5%

condition
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size851.0 KiB
3
13911 
4
5645 
5
1687 
2
 
164
1
 
29

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters21436
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row5
5th row3

Common Values

ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Length

2024-11-26T22:09:29.837673image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-26T22:09:29.932361image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring characters

ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 13911
64.9%
4 5645
26.3%
5 1687
 
7.9%
2 164
 
0.8%
1 29
 
0.1%

grade
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6617373
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:30.022766image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range12
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1742565
Coefficient of variation (CV)0.15326243
Kurtosis1.1902695
Mean7.6617373
Median Absolute Deviation (MAD)1
Skewness0.77035741
Sum164237
Variance1.3788783
MonotonicityNot monotonic
2024-11-26T22:09:30.112889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 8896
41.5%
8 6044
28.2%
9 2606
 
12.2%
6 1995
 
9.3%
10 1130
 
5.3%
11 396
 
1.8%
5 234
 
1.1%
12 89
 
0.4%
4 29
 
0.1%
13 13
 
0.1%
Other values (2) 4
 
< 0.1%
ValueCountFrequency (%)
1 1
 
< 0.1%
3 3
 
< 0.1%
4 29
 
0.1%
5 234
 
1.1%
6 1995
 
9.3%
7 8896
41.5%
8 6044
28.2%
9 2606
 
12.2%
10 1130
 
5.3%
11 396
 
1.8%
ValueCountFrequency (%)
13 13
 
0.1%
12 89
 
0.4%
11 396
 
1.8%
10 1130
 
5.3%
9 2606
 
12.2%
8 6044
28.2%
7 8896
41.5%
6 1995
 
9.3%
5 234
 
1.1%
4 29
 
0.1%

sqft_above
Real number (ℝ)

HIGH CORRELATION 

Distinct946
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1790.9604
Minimum290
Maximum9410
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:30.222112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum290
5-th percentile850
Q11200
median1560
Q32220
95-th percentile3400
Maximum9410
Range9120
Interquartile range (IQR)1020

Descriptive statistics

Standard deviation829.02649
Coefficient of variation (CV)0.46289492
Kurtosis3.3950819
Mean1790.9604
Median Absolute Deviation (MAD)450
Skewness1.4442209
Sum38391028
Variance687284.92
MonotonicityNot monotonic
2024-11-26T22:09:30.341866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1300 210
 
1.0%
1010 204
 
1.0%
1200 203
 
0.9%
1220 186
 
0.9%
1140 183
 
0.9%
1400 180
 
0.8%
1060 177
 
0.8%
1180 177
 
0.8%
1340 174
 
0.8%
1250 173
 
0.8%
Other values (936) 19569
91.3%
ValueCountFrequency (%)
290 1
< 0.1%
370 1
< 0.1%
380 1
< 0.1%
384 1
< 0.1%
390 2
< 0.1%
410 1
< 0.1%
420 2
< 0.1%
430 1
< 0.1%
440 1
< 0.1%
460 1
< 0.1%
ValueCountFrequency (%)
9410 1
< 0.1%
8860 1
< 0.1%
8570 1
< 0.1%
8020 1
< 0.1%
7880 1
< 0.1%
7850 1
< 0.1%
7680 1
< 0.1%
7420 1
< 0.1%
7320 1
< 0.1%
6720 1
< 0.1%

sqft_basement
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct306
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.7445
Minimum0
Maximum4820
Zeros13015
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:30.464021image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3560
95-th percentile1190
Maximum4820
Range4820
Interquartile range (IQR)560

Descriptive statistics

Standard deviation442.78198
Coefficient of variation (CV)1.5177047
Kurtosis2.7119878
Mean291.7445
Median Absolute Deviation (MAD)0
Skewness1.5768731
Sum6253835
Variance196055.88
MonotonicityNot monotonic
2024-11-26T22:09:30.588289image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 13015
60.7%
600 220
 
1.0%
700 215
 
1.0%
500 211
 
1.0%
800 206
 
1.0%
400 184
 
0.9%
1000 147
 
0.7%
900 143
 
0.7%
300 141
 
0.7%
480 106
 
0.5%
Other values (296) 6848
31.9%
ValueCountFrequency (%)
0 13015
60.7%
10 1
 
< 0.1%
20 1
 
< 0.1%
40 4
 
< 0.1%
50 11
 
0.1%
60 10
 
< 0.1%
65 1
 
< 0.1%
70 7
 
< 0.1%
80 20
 
0.1%
90 21
 
0.1%
ValueCountFrequency (%)
4820 1
< 0.1%
4130 1
< 0.1%
3500 1
< 0.1%
3480 1
< 0.1%
3260 1
< 0.1%
3000 1
< 0.1%
2850 1
< 0.1%
2810 1
< 0.1%
2730 1
< 0.1%
2720 1
< 0.1%

yr_built
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1971.0984
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:30.707825image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1915
Q11952
median1975
Q31997
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.385277
Coefficient of variation (CV)0.014908072
Kurtosis-0.65434573
Mean1971.0984
Median Absolute Deviation (MAD)23
Skewness-0.47457738
Sum42252466
Variance863.49449
MonotonicityNot monotonic
2024-11-26T22:09:30.837864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014 559
 
2.6%
2006 454
 
2.1%
2005 450
 
2.1%
2004 429
 
2.0%
2003 422
 
2.0%
2007 415
 
1.9%
1977 415
 
1.9%
1978 384
 
1.8%
1968 379
 
1.8%
2008 367
 
1.7%
Other values (106) 17162
80.1%
ValueCountFrequency (%)
1900 86
0.4%
1901 29
 
0.1%
1902 27
 
0.1%
1903 45
0.2%
1904 44
0.2%
1905 74
0.3%
1906 91
0.4%
1907 65
0.3%
1908 86
0.4%
1909 94
0.4%
ValueCountFrequency (%)
2015 38
 
0.2%
2014 559
2.6%
2013 199
 
0.9%
2012 169
 
0.8%
2011 130
 
0.6%
2010 143
 
0.7%
2009 229
1.1%
2008 367
1.7%
2007 415
1.9%
2006 454
2.1%

yr_renovated
Real number (ℝ)

ZEROS 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.7298
Minimum0
Maximum2015
Zeros20526
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:30.964632image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation402.43101
Coefficient of variation (CV)4.7495806
Kurtosis18.610747
Mean84.7298
Median Absolute Deviation (MAD)0
Skewness4.5395471
Sum1816268
Variance161950.72
MonotonicityNot monotonic
2024-11-26T22:09:31.097601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 20526
95.8%
2014 91
 
0.4%
2013 37
 
0.2%
2003 36
 
0.2%
2005 35
 
0.2%
2007 35
 
0.2%
2000 35
 
0.2%
2004 26
 
0.1%
1990 24
 
0.1%
2006 24
 
0.1%
Other values (60) 567
 
2.6%
ValueCountFrequency (%)
0 20526
95.8%
1934 1
 
< 0.1%
1940 2
 
< 0.1%
1944 1
 
< 0.1%
1945 3
 
< 0.1%
1946 2
 
< 0.1%
1948 1
 
< 0.1%
1950 2
 
< 0.1%
1951 1
 
< 0.1%
1953 3
 
< 0.1%
ValueCountFrequency (%)
2015 16
 
0.1%
2014 91
0.4%
2013 37
0.2%
2012 11
 
0.1%
2011 13
 
0.1%
2010 18
 
0.1%
2009 22
 
0.1%
2008 18
 
0.1%
2007 35
 
0.2%
2006 24
 
0.1%

zipcode
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98077.862
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:31.228025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198033
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.469371
Coefficient of variation (CV)0.00054517268
Kurtosis-0.84968929
Mean98077.862
Median Absolute Deviation (MAD)42
Skewness0.4081288
Sum2.1023971 × 109
Variance2858.9736
MonotonicityNot monotonic
2024-11-26T22:09:31.370823image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103 600
 
2.8%
98038 587
 
2.7%
98115 576
 
2.7%
98052 571
 
2.7%
98117 548
 
2.6%
98042 547
 
2.6%
98034 543
 
2.5%
98118 499
 
2.3%
98023 492
 
2.3%
98006 490
 
2.3%
Other values (60) 15983
74.6%
ValueCountFrequency (%)
98001 359
1.7%
98002 197
0.9%
98003 276
1.3%
98004 315
1.5%
98005 168
 
0.8%
98006 490
2.3%
98007 139
 
0.6%
98008 283
1.3%
98010 99
 
0.5%
98011 194
 
0.9%
ValueCountFrequency (%)
98199 316
1.5%
98198 275
1.3%
98188 135
 
0.6%
98178 258
1.2%
98177 254
1.2%
98168 264
1.2%
98166 250
1.2%
98155 442
2.1%
98148 56
 
0.3%
98146 281
1.3%

lat
Real number (ℝ)

HIGH CORRELATION 

Distinct5034
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.560156
Minimum47.1559
Maximum47.7776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:31.505492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum47.1559
5-th percentile47.3103
Q147.4711
median47.572
Q347.678
95-th percentile47.749625
Maximum47.7776
Range0.6217
Interquartile range (IQR)0.2069

Descriptive statistics

Standard deviation0.13860131
Coefficient of variation (CV)0.0029142317
Kurtosis-0.67359967
Mean47.560156
Median Absolute Deviation (MAD)0.10475
Skewness-0.4881646
Sum1019499.5
Variance0.019210324
MonotonicityNot monotonic
2024-11-26T22:09:31.630743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.5491 17
 
0.1%
47.6624 17
 
0.1%
47.6846 17
 
0.1%
47.5322 17
 
0.1%
47.6955 16
 
0.1%
47.6711 16
 
0.1%
47.6886 16
 
0.1%
47.6904 15
 
0.1%
47.6647 15
 
0.1%
47.686 15
 
0.1%
Other values (5024) 21275
99.2%
ValueCountFrequency (%)
47.1559 1
< 0.1%
47.1593 1
< 0.1%
47.1622 1
< 0.1%
47.1647 1
< 0.1%
47.1764 1
< 0.1%
47.1775 1
< 0.1%
47.1776 2
< 0.1%
47.1795 1
< 0.1%
47.1803 1
< 0.1%
47.1808 1
< 0.1%
ValueCountFrequency (%)
47.7776 3
< 0.1%
47.7775 3
< 0.1%
47.7774 1
 
< 0.1%
47.7772 3
< 0.1%
47.7771 2
 
< 0.1%
47.777 2
 
< 0.1%
47.7769 3
< 0.1%
47.7768 2
 
< 0.1%
47.7767 6
< 0.1%
47.7766 4
< 0.1%

long
Real number (ℝ)

HIGH CORRELATION 

Distinct752
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-122.2137
Minimum-122.519
Maximum-121.315
Zeros0
Zeros (%)0.0%
Negative21436
Negative (%)100.0%
Memory size851.0 KiB
2024-11-26T22:09:31.760593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-122.519
5-th percentile-122.387
Q1-122.328
median-122.23
Q3-122.124
95-th percentile-121.979
Maximum-121.315
Range1.204
Interquartile range (IQR)0.204

Descriptive statistics

Standard deviation0.14089598
Coefficient of variation (CV)-0.0011528657
Kurtosis1.0455319
Mean-122.2137
Median Absolute Deviation (MAD)0.101
Skewness0.88192326
Sum-2619772.8
Variance0.019851678
MonotonicityNot monotonic
2024-11-26T22:09:31.886055image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-122.29 114
 
0.5%
-122.3 110
 
0.5%
-122.362 102
 
0.5%
-122.291 100
 
0.5%
-122.363 99
 
0.5%
-122.372 98
 
0.5%
-122.357 95
 
0.4%
-122.288 95
 
0.4%
-122.365 94
 
0.4%
-122.346 93
 
0.4%
Other values (742) 20436
95.3%
ValueCountFrequency (%)
-122.519 1
 
< 0.1%
-122.515 1
 
< 0.1%
-122.514 1
 
< 0.1%
-122.512 1
 
< 0.1%
-122.511 2
< 0.1%
-122.509 2
< 0.1%
-122.507 1
 
< 0.1%
-122.506 1
 
< 0.1%
-122.505 3
< 0.1%
-122.504 2
< 0.1%
ValueCountFrequency (%)
-121.315 2
< 0.1%
-121.316 1
< 0.1%
-121.319 1
< 0.1%
-121.321 1
< 0.1%
-121.325 1
< 0.1%
-121.352 2
< 0.1%
-121.359 1
< 0.1%
-121.364 2
< 0.1%
-121.402 1
< 0.1%
-121.403 1
< 0.1%

sqft_living15
Real number (ℝ)

HIGH CORRELATION 

Distinct777
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1988.3144
Minimum399
Maximum6210
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:32.004216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum399
5-th percentile1140
Q11490
median1840
Q32370
95-th percentile3300
Maximum6210
Range5811
Interquartile range (IQR)880

Descriptive statistics

Standard deviation685.69909
Coefficient of variation (CV)0.34486452
Kurtosis1.593321
Mean1988.3144
Median Absolute Deviation (MAD)410
Skewness1.1058446
Sum42621507
Variance470183.25
MonotonicityNot monotonic
2024-11-26T22:09:32.118867image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1540 193
 
0.9%
1440 190
 
0.9%
1560 190
 
0.9%
1500 179
 
0.8%
1460 168
 
0.8%
1800 166
 
0.8%
1720 165
 
0.8%
1580 165
 
0.8%
1610 164
 
0.8%
1620 163
 
0.8%
Other values (767) 19693
91.9%
ValueCountFrequency (%)
399 1
 
< 0.1%
460 2
 
< 0.1%
620 2
 
< 0.1%
670 1
 
< 0.1%
690 2
 
< 0.1%
700 2
 
< 0.1%
710 2
 
< 0.1%
720 2
 
< 0.1%
740 8
< 0.1%
750 3
 
< 0.1%
ValueCountFrequency (%)
6210 1
 
< 0.1%
6110 1
 
< 0.1%
5790 6
< 0.1%
5610 1
 
< 0.1%
5600 1
 
< 0.1%
5500 1
 
< 0.1%
5380 1
 
< 0.1%
5340 1
 
< 0.1%
5330 1
 
< 0.1%
5220 1
 
< 0.1%

sqft_lot15
Real number (ℝ)

HIGH CORRELATION 

Distinct8689
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12785.961
Minimum651
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size851.0 KiB
2024-11-26T22:09:32.243755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum651
5-th percentile1989.5
Q15100
median7620
Q310087.25
95-th percentile37197
Maximum871200
Range870549
Interquartile range (IQR)4987.25

Descriptive statistics

Standard deviation27375.467
Coefficient of variation (CV)2.1410567
Kurtosis150.32404
Mean12785.961
Median Absolute Deviation (MAD)2508.5
Skewness9.4953799
Sum2.7407987 × 108
Variance7.4941622 × 108
MonotonicityNot monotonic
2024-11-26T22:09:32.361938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5000 425
 
2.0%
4000 355
 
1.7%
6000 286
 
1.3%
7200 210
 
1.0%
4800 144
 
0.7%
7500 142
 
0.7%
8400 115
 
0.5%
4500 110
 
0.5%
3600 110
 
0.5%
5100 109
 
0.5%
Other values (8679) 19430
90.6%
ValueCountFrequency (%)
651 1
 
< 0.1%
659 1
 
< 0.1%
660 1
 
< 0.1%
748 2
< 0.1%
750 4
< 0.1%
755 1
 
< 0.1%
757 1
 
< 0.1%
758 1
 
< 0.1%
788 1
 
< 0.1%
794 1
 
< 0.1%
ValueCountFrequency (%)
871200 1
< 0.1%
858132 1
< 0.1%
560617 1
< 0.1%
438213 1
< 0.1%
434728 1
< 0.1%
425581 1
< 0.1%
422967 1
< 0.1%
411962 1
< 0.1%
392040 2
< 0.1%
386812 1
< 0.1%

Interactions

2024-11-26T22:09:24.682755image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:56.636014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:58.183710image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:59.789212image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:01.361752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:04.067997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:05.626096image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:07.397489image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:08.875108image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:10.389574image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:12.254472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-11-26T22:09:09.949082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:11.457671image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:13.410862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:15.111879image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:16.738688image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:18.748350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:20.486736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:22.197925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:24.116138image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:25.951791image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:57.833914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:59.412498image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:01.013749image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:03.705721image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:05.262447image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:07.038704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:08.540704image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:10.046353image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:11.892151image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:13.536577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:15.209090image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:16.842105image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:18.848941image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:20.597225image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:22.322442image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:24.236275image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:26.040578image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:57.931554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:59.504862image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:01.104554image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:03.801584image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:05.353576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:07.130682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:08.626549image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:10.133535image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:11.999194image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:13.643952image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:15.299579image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:16.934676image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:18.947864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:20.701587image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:22.429661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:24.349532image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:26.134031image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:58.015653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:59.602407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:01.190558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:03.896812image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:05.443147image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:07.219920image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:08.709190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:10.220557image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:12.089287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:13.751681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:15.390598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:17.026854image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:19.045996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:20.803358image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:22.538525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:24.456383image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:26.227577image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:58.101996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:08:59.694880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:01.279604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:03.983248image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:05.537479image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:07.309007image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:08.793653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:10.308718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:12.171818image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:13.853735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:15.477637image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:17.119663image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:19.149824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:20.901480image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:22.630548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-11-26T22:09:24.564743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-11-26T22:09:32.453583image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
bathroomsbedroomsconditionfloorsgradeidlatlongpricesqft_abovesqft_basementsqft_livingsqft_living15sqft_lotsqft_lot15viewwaterfrontyr_builtyr_renovatedzipcode
bathrooms1.0000.5220.1300.5460.6570.0140.0070.2600.4950.6900.1910.7450.5700.0690.0630.1140.1020.5660.043-0.203
bedrooms0.5221.0000.0230.2270.3820.006-0.0230.1910.3450.5400.2300.6480.4450.2170.2020.0380.0000.1790.017-0.168
condition0.1300.0231.000-0.291-0.171-0.024-0.023-0.0870.017-0.1610.162-0.065-0.0890.1150.1180.0240.017-0.398-0.067-0.021
floors0.5460.227-0.2911.0000.5010.0180.0240.1480.3200.598-0.2730.4000.305-0.235-0.2320.0230.0220.5520.012-0.060
grade0.6570.382-0.1710.5011.0000.0180.1020.2210.6560.7120.0920.7160.6620.1530.1570.1420.1180.4990.016-0.179
id0.0140.006-0.0240.0180.0181.000-0.0050.0060.0020.0020.0010.000-0.001-0.117-0.1150.0280.0060.026-0.018-0.005
lat0.007-0.023-0.0230.0240.102-0.0051.000-0.1440.456-0.0270.1140.0290.026-0.121-0.1160.0680.034-0.1260.0250.249
long0.2600.191-0.0870.1480.2210.006-0.1441.0000.0610.385-0.2010.2840.3800.3710.3730.0850.0970.411-0.076-0.578
price0.4950.3450.0170.3200.6560.0020.4560.0611.0000.5400.2520.6440.5720.0760.0640.2080.3200.0980.102-0.006
sqft_above0.6900.540-0.1610.5980.7120.002-0.0270.3850.5401.000-0.1670.8440.6970.2730.2550.0890.0820.4710.030-0.277
sqft_basement0.1910.2300.162-0.2730.0920.0010.114-0.2010.252-0.1671.0000.3270.1290.0370.0310.1590.135-0.1800.0630.115
sqft_living0.7450.648-0.0650.4000.7160.0000.0290.2840.6440.8440.3271.0000.7470.3060.2850.1480.1400.3510.052-0.206
sqft_living150.5700.445-0.0890.3050.662-0.0010.0260.3800.5720.6970.1290.7471.0000.3610.3670.1460.0890.334-0.006-0.287
sqft_lot0.0690.2170.115-0.2350.153-0.117-0.1210.3710.0760.2730.0370.3060.3611.0000.9230.0400.014-0.0390.008-0.320
sqft_lot150.0630.2020.118-0.2320.157-0.115-0.1160.3730.0640.2550.0310.2850.3670.9231.0000.0350.000-0.0180.009-0.327
view0.1140.0380.0240.0230.1420.0280.0680.0850.2080.0890.1590.1480.1460.0400.0351.0000.595-0.0680.0960.080
waterfront0.1020.0000.0170.0220.1180.0060.0340.0970.3200.0820.1350.1400.0890.0140.0000.5951.000-0.0290.0920.030
yr_built0.5660.179-0.3980.5520.4990.026-0.1260.4110.0980.471-0.1800.3510.334-0.039-0.018-0.068-0.0291.000-0.215-0.315
yr_renovated0.0430.017-0.0670.0120.016-0.0180.025-0.0760.1020.0300.0630.052-0.0060.0080.0090.0960.092-0.2151.0000.062
zipcode-0.203-0.168-0.021-0.060-0.179-0.0050.249-0.578-0.006-0.2770.115-0.206-0.287-0.320-0.3270.0800.030-0.3150.0621.000
2024-11-26T22:09:32.646576image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.006-0.0180.0010.004-0.013-0.1330.018-0.0030.011-0.0240.006-0.012-0.0060.021-0.017-0.008-0.0030.019-0.004-0.140
date0.0061.000-0.006-0.018-0.036-0.0360.006-0.0240.001-0.002-0.052-0.042-0.029-0.019-0.002-0.0250.003-0.033-0.008-0.0320.002
price-0.018-0.0061.0000.3090.5240.7010.0890.2550.2670.3970.0350.6660.6050.3240.0510.127-0.0510.3070.0190.5840.082
bedrooms0.001-0.0180.3091.0000.5170.5780.0320.175-0.0070.0800.0280.3580.4780.3030.1540.018-0.154-0.0100.1300.3930.029
bathrooms0.004-0.0360.5240.5171.0000.7540.0870.5000.0640.187-0.1280.6650.6850.2840.5050.051-0.2030.0230.2220.5680.087
sqft_living-0.013-0.0360.7010.5780.7541.0000.1720.3530.1040.284-0.0610.7620.8770.4340.3170.055-0.1990.0510.2390.7560.183
sqft_lot-0.1330.0060.0890.0320.0870.1721.000-0.0060.0220.075-0.0090.1130.1830.0150.0520.008-0.129-0.0860.2300.1440.718
floors0.018-0.0240.2550.1750.5000.353-0.0061.0000.0230.028-0.2670.4570.523-0.2460.4890.006-0.0580.0490.1240.279-0.012
waterfront-0.0030.0010.267-0.0070.0640.1040.0220.0231.0000.4030.0170.0830.0720.081-0.0270.0930.031-0.014-0.0420.0870.031
view0.011-0.0020.3970.0800.1870.2840.0750.0280.4031.0000.0450.2500.1670.276-0.0550.1040.0870.006-0.0800.2790.073
condition-0.024-0.0520.0350.028-0.128-0.061-0.009-0.2670.0170.0451.000-0.148-0.1610.174-0.365-0.0610.005-0.015-0.108-0.095-0.004
grade0.006-0.0420.6660.3580.6650.7620.1130.4570.0830.250-0.1481.0000.7560.1670.4450.014-0.1830.1130.1970.7130.118
sqft_above-0.012-0.0290.6050.4780.6850.8770.1830.5230.0720.167-0.1610.7561.000-0.0520.4230.023-0.260-0.0020.3430.7320.193
sqft_basement-0.006-0.0190.3240.3030.2840.4340.015-0.2460.0810.2760.1740.167-0.0521.000-0.1350.0720.0750.109-0.1460.1990.017
yr_built0.021-0.0020.0510.1540.5050.3170.0520.489-0.027-0.055-0.3650.4450.423-0.1351.000-0.226-0.346-0.1490.4090.3250.070
yr_renovated-0.017-0.0250.1270.0180.0510.0550.0080.0060.0930.104-0.0610.0140.0230.072-0.2261.0000.0640.029-0.069-0.0030.008
zipcode-0.0080.003-0.051-0.154-0.203-0.199-0.129-0.0580.0310.0870.005-0.183-0.2600.075-0.3460.0641.0000.267-0.565-0.278-0.147
lat-0.003-0.0330.307-0.0100.0230.051-0.0860.049-0.0140.006-0.0150.113-0.0020.109-0.1490.0290.2671.000-0.1360.048-0.087
long0.019-0.0080.0190.1300.2220.2390.2300.124-0.042-0.080-0.1080.1970.343-0.1460.409-0.069-0.565-0.1361.0000.3340.254
sqft_living15-0.004-0.0320.5840.3930.5680.7560.1440.2790.0870.279-0.0950.7130.7320.1990.325-0.003-0.2780.0480.3341.0000.182
sqft_lot15-0.1400.0020.0820.0290.0870.1830.718-0.0120.0310.073-0.0040.1180.1930.0170.0700.008-0.147-0.0870.2540.1821.000
2024-11-26T22:09:32.851986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.0060.0020.0060.0140.000-0.1170.018-0.0040.012-0.0240.0180.0020.0010.026-0.018-0.005-0.0050.006-0.001-0.115
date0.0061.000-0.014-0.018-0.035-0.036-0.013-0.0250.002-0.000-0.046-0.043-0.027-0.014-0.003-0.0250.002-0.030-0.006-0.029-0.013
price0.002-0.0141.0000.3450.4950.6440.0760.3200.1150.2930.0170.6560.5400.2520.0980.102-0.0060.4560.0610.5720.064
bedrooms0.006-0.0180.3451.0000.5220.6480.2170.227-0.0080.0810.0120.3820.5400.2300.1790.017-0.168-0.0230.1910.4450.202
bathrooms0.014-0.0350.4950.5221.0000.7450.0690.5460.0500.155-0.1660.6570.6900.1910.5660.043-0.2030.0070.2600.5700.063
sqft_living0.000-0.0360.6440.6480.7451.0000.3060.4000.0700.232-0.0650.7160.8440.3270.3510.052-0.2060.0290.2840.7470.285
sqft_lot-0.117-0.0130.0760.2170.0690.3061.000-0.2350.0860.1170.1150.1530.2730.037-0.0390.008-0.320-0.1210.3710.3610.923
floors0.018-0.0250.3200.2270.5460.400-0.2351.0000.0240.019-0.2910.5010.598-0.2730.5520.012-0.0600.0240.1480.305-0.232
waterfront-0.0040.0020.115-0.0080.0500.0700.0860.0241.0000.2860.0170.0620.0540.052-0.0290.0920.030-0.019-0.0380.0750.093
view0.012-0.0000.2930.0810.1550.2320.1170.0190.2861.0000.0460.2160.1430.236-0.0680.0960.080-0.001-0.1040.2550.117
condition-0.024-0.0460.0170.012-0.166-0.0650.115-0.2910.0170.0461.000-0.171-0.1610.162-0.398-0.067-0.021-0.023-0.087-0.0890.118
grade0.018-0.0430.6560.3820.6570.7160.1530.5010.0620.216-0.1711.0000.7120.0920.4990.016-0.1790.1020.2210.6620.157
sqft_above0.002-0.0270.5400.5400.6900.8440.2730.5980.0540.143-0.1610.7121.000-0.1670.4710.030-0.277-0.0270.3850.6970.255
sqft_basement0.001-0.0140.2520.2300.1910.3270.037-0.2730.0520.2360.1620.092-0.1671.000-0.1800.0630.1150.114-0.2010.1290.031
yr_built0.026-0.0030.0980.1790.5660.351-0.0390.552-0.029-0.068-0.3980.4990.471-0.1801.000-0.215-0.315-0.1260.4110.334-0.018
yr_renovated-0.018-0.0250.1020.0170.0430.0520.0080.0120.0920.096-0.0670.0160.0300.063-0.2151.0000.0620.025-0.076-0.0060.009
zipcode-0.0050.002-0.006-0.168-0.203-0.206-0.320-0.0600.0300.080-0.021-0.179-0.2770.115-0.3150.0621.0000.249-0.578-0.287-0.327
lat-0.005-0.0300.456-0.0230.0070.029-0.1210.024-0.019-0.001-0.0230.102-0.0270.114-0.1260.0250.2491.000-0.1440.026-0.116
long0.006-0.0060.0610.1910.2600.2840.3710.148-0.038-0.104-0.0870.2210.385-0.2010.411-0.076-0.578-0.1441.0000.3800.373
sqft_living15-0.001-0.0290.5720.4450.5700.7470.3610.3050.0750.255-0.0890.6620.6970.1290.334-0.006-0.2870.0260.3801.0000.367
sqft_lot15-0.115-0.0130.0640.2020.0630.2850.923-0.2320.0930.1170.1180.1570.2550.031-0.0180.009-0.327-0.1160.3730.3671.000
2024-11-26T22:09:33.049008image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.0040.0020.0050.0100.000-0.0790.014-0.0030.010-0.0190.0140.0020.0010.018-0.014-0.004-0.0030.004-0.001-0.078
date0.0041.000-0.009-0.014-0.025-0.024-0.009-0.0190.001-0.000-0.037-0.032-0.018-0.010-0.002-0.0200.001-0.020-0.004-0.019-0.008
price0.002-0.0091.0000.2660.3680.4630.0480.2500.0940.2370.0130.5260.3770.1910.0670.082-0.0080.2930.0380.4040.039
bedrooms0.005-0.0140.2661.0000.4400.5220.1650.201-0.0070.0740.0110.3320.4300.1960.1410.015-0.128-0.0170.1440.3470.154
bathrooms0.010-0.0250.3680.4401.0000.5880.0500.4510.0430.132-0.1400.5560.5300.1530.4280.037-0.1430.0050.1870.4300.046
sqft_living0.000-0.0240.4630.5220.5881.0000.2080.3150.0580.187-0.0520.5860.6970.2650.2430.042-0.1380.0190.1910.5710.193
sqft_lot-0.079-0.0090.0480.1650.0500.2081.000-0.1870.0700.0940.0900.1120.1860.028-0.0060.007-0.206-0.0800.2580.2450.793
floors0.014-0.0190.2500.2010.4510.315-0.1871.0000.0230.018-0.2680.4350.475-0.2360.4180.011-0.0490.0190.1150.239-0.184
waterfront-0.0030.0010.094-0.0070.0430.0580.0700.0231.0000.2810.0160.0570.0450.047-0.0240.0910.025-0.016-0.0310.0610.076
view0.010-0.0000.2370.0740.1320.1870.0940.0180.2811.0000.0430.1950.1150.212-0.0550.0940.065-0.001-0.0840.2060.094
condition-0.019-0.0370.0130.011-0.140-0.0520.090-0.2680.0160.0431.000-0.152-0.1280.143-0.322-0.064-0.018-0.018-0.069-0.0710.093
grade0.014-0.0320.5260.3320.5560.5860.1120.4350.0570.195-0.1521.0000.5810.0780.3850.014-0.1350.0720.1650.5360.115
sqft_above0.002-0.0180.3770.4300.5300.6970.1860.4750.0450.115-0.1280.5811.000-0.1190.3320.024-0.182-0.0180.2610.5160.174
sqft_basement0.001-0.0100.1910.1960.1530.2650.028-0.2360.0470.2120.1430.078-0.1191.000-0.1310.0570.0880.084-0.1470.1020.024
yr_built0.018-0.0020.0670.1410.4280.243-0.0060.418-0.024-0.055-0.3220.3850.332-0.1311.000-0.175-0.185-0.0840.2970.2310.008
yr_renovated-0.014-0.0200.0820.0150.0370.0420.0070.0110.0910.094-0.0640.0140.0240.057-0.1751.0000.0510.020-0.061-0.0050.007
zipcode-0.0040.001-0.008-0.128-0.143-0.138-0.206-0.0490.0250.065-0.018-0.135-0.1820.088-0.1850.0511.0000.176-0.364-0.191-0.209
lat-0.003-0.0200.293-0.0170.0050.019-0.0800.019-0.016-0.001-0.0180.072-0.0180.084-0.0840.0200.1761.000-0.0950.016-0.076
long0.004-0.0040.0380.1440.1870.1910.2580.115-0.031-0.084-0.0690.1650.261-0.1470.297-0.061-0.364-0.0951.0000.2580.261
sqft_living15-0.001-0.0190.4040.3470.4300.5710.2450.2390.0610.206-0.0710.5360.5160.1020.231-0.005-0.1910.0160.2581.0000.250
sqft_lot15-0.078-0.0080.0390.1540.0460.1930.793-0.1840.0760.0940.0930.1150.1740.0240.0080.007-0.209-0.0760.2610.2501.000
2024-11-26T22:09:33.252654image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
idpricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
id1.0000.0730.0420.0790.0790.0840.0800.0080.0670.0720.1100.0870.0620.1610.0370.3500.2660.3250.1180.095
price0.0731.0000.2960.7420.8980.0390.2060.4190.4610.0550.6560.7910.7930.1140.1670.1760.3410.1470.5430.000
bedrooms0.0420.2961.0000.5140.5780.0000.2060.0000.1000.0620.3730.5050.3590.2150.0160.2020.1100.2080.4400.000
bathrooms0.0790.7420.5141.0000.8720.0780.4680.1330.2670.3020.7170.8460.7310.5910.0780.3350.2430.3410.6140.093
sqft_living0.0790.8980.5780.8721.0000.0980.3610.1830.3410.1420.7510.9120.9160.3680.0930.3130.2090.3000.7440.115
sqft_lot0.0840.0390.0000.0780.0981.0000.0360.0140.0700.0710.0920.1310.0570.0760.0000.0870.1340.1700.0590.628
floors0.0800.2060.2060.4680.3610.0361.0000.0310.0350.2610.3710.4910.2270.6200.0830.3360.2480.3020.3400.035
waterfront0.0080.4190.0000.1330.1830.0140.0311.0000.4900.0140.1530.1070.1760.0440.1440.1040.0450.1260.1170.000
view0.0670.4610.1000.2670.3410.0700.0350.4901.0000.0650.3160.2110.3630.1030.0890.1780.1620.2010.3370.057
condition0.0720.0550.0620.3020.1420.0710.2610.0140.0651.0000.3450.2510.2230.5320.0550.1750.1360.1910.1490.022
grade0.1100.6560.3730.7170.7510.0920.3710.1530.3160.3451.0000.7600.3590.4970.0180.3220.3300.2940.7280.079
sqft_above0.0870.7910.5050.8460.9120.1310.4910.1070.2110.2510.7601.0000.6540.4980.0410.4050.2010.3900.7470.153
sqft_basement0.0620.7930.3590.7310.9160.0570.2270.1760.3630.2230.3590.6541.0000.2870.1020.1900.1570.1880.3830.090
yr_built0.1610.1140.2150.5910.3680.0760.6200.0440.1030.5320.4970.4980.2871.0000.3030.6200.4450.5420.4040.065
yr_renovated0.0370.1670.0160.0780.0930.0000.0830.1440.0890.0550.0180.0410.1020.3031.0000.1120.0770.1080.0000.000
zipcode0.3500.1760.2020.3350.3130.0870.3360.1040.1780.1750.3220.4050.1900.6200.1121.0000.7920.7860.4420.113
lat0.2660.3410.1100.2430.2090.1340.2480.0450.1620.1360.3300.2010.1570.4450.0770.7921.0000.4900.2840.142
long0.3250.1470.2080.3410.3000.1700.3020.1260.2010.1910.2940.3900.1880.5420.1080.7860.4901.0000.4320.194
sqft_living150.1180.5430.4400.6140.7440.0590.3400.1170.3370.1490.7280.7470.3830.4040.0000.4420.2840.4321.0000.086
sqft_lot150.0950.0000.0000.0930.1150.6280.0350.0000.0570.0220.0790.1530.0900.0650.0000.1130.1420.1940.0861.000
2024-11-26T22:09:33.416233image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
conditionviewwaterfront
condition1.0000.0240.017
view0.0241.0000.595
waterfront0.0170.5951.000

Missing values

2024-11-26T22:09:26.385047image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-26T22:09:26.658545image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
071293005202014-10-13221900.031.00118056501.0003711800195509817847.5112-122.25713405650
164141001922014-12-09538000.032.25257072422.000372170400195119919812547.7210-122.31916907639
256315004002015-02-25180000.021.00770100001.000367700193309802847.7379-122.23327208062
324872008752014-12-09604000.043.00196050001.000571050910196509813647.5208-122.39313605000
419544005102015-02-18510000.032.00168080801.0003816800198709807447.6168-122.04518007503
572375503102014-05-121225000.044.5054201019301.00031138901530200109805347.6561-122.0054760101930
613214000602014-06-27257500.032.25171568192.0003717150199509800347.3097-122.32722386819
720080002702015-01-15291850.031.50106097111.0003710600196309819847.4095-122.31516509711
824146001262015-04-15229500.031.00178074701.000371050730196009814647.5123-122.33717808113
937935001602015-03-12323000.032.50189065602.0003718900200309803847.3684-122.03123907570
iddatepricebedroomsbathroomssqft_livingsqft_lotfloorswaterfrontviewconditiongradesqft_abovesqft_basementyr_builtyr_renovatedzipcodelatlongsqft_living15sqft_lot15
2160378521400402014-08-25507250.032.50227055362.0003822700200309806547.5389-121.88122705731
2160498342013672015-01-26429000.032.00149011263.0003814900201409814447.5699-122.28814001230
2160534489002102014-10-14610685.042.50252060232.0003925200201409805647.5137-122.16725206023
2160679360004292015-03-261007500.043.50351072002.000392600910200909813647.5537-122.39820506200
2160729978000212015-02-19475000.032.50131012942.000381180130200809811647.5773-122.40913301265
216082630000182014-05-21360000.032.50153011313.0003815300200909810347.6993-122.34615301509
2160966000601202015-02-23400000.042.50231058132.0003823100201409814647.5107-122.36218307200
2161015233001412014-06-23402101.020.75102013502.0003710200200909814447.5944-122.29910202007
216112913101002015-01-16400000.032.50160023882.0003816000200409802747.5345-122.06914101287
2161215233001572014-10-15325000.020.75102010762.0003710200200809814447.5941-122.29910201357